01 13 3
INTERNATIONAL WORKING PAPER SERIES
FOOD AND RESOURCE ECONOMICS DEPARTMENT
Institute of Food and Agricultural Sciences
University of Florida
Gainesville, Florida 32611
APPROACHES TO TECHNOLOGY IMPACT
ASSESSMENT IN THE CARIBBEAN AGRICULTURAL
RESEARCH AND DEVELOPMENT INSTITUTE
Max R. Langham, Carlton G. Davis
and Clyde F. Kiker
APPROACHES TO TECHNOLOGY IMPACT ASSESSMENT
IN THE CARIBBEAN AGRICULTURAL RESEARCH
AND DEVELOPMENT INSTITUTE'
Max R. Langham, Carlton G. Davis, and Clyde F. Kiker
Food and Resource Economics Department
University of Florida, Gainesville FL 32611-0240, USA
Technology impact assessment can be broadly defined as the
process of identifying current or future consequences of
technology. New technologies can impact a sub-sector, a sector,
producing firms, a market, an economy, and consumers all internal
to a country and trading partners (and the consumers they
represent) external. Also, economists, like practitioners in many
other sciences, are becoming increasingly appreciative of the
importance of our environment and the interdependencies of all life
forms. This increasing awareness requires that we also assess the
impact of our technologies with respect to the sustainability of
our common life support system.
At the same time, we are becoming increasingly cognizant of
the fact that in the final analysis, the outcome of technological
change is influenced by the institutional and policy environments
within which it is introduced. Where such environments have been
favorable, benefits have been more widely distributed, but where
they have been unfavorable and appropriate changes have not been
made, potential benefits have not been fully exploited (Pinstrup-
Andersen and Hazell). Even in the most favorable environments for
technical change, some producers will find it difficult or
impossible to adjust to the new knowledge. Most often, these
people left behind are the poorest of the poor, and historically,
*Paper prepared for the CARDI sponsored workshop on
Identification and Development of Methodologies for Technology
Impact Assessment, Trinidad, WI, November 16-19, 1993.
most economies have not been very sensitive to the needs of those
bearing this cost of change.
The impacts on producers and consumers of food differ markedly
by income classes. Normally, poor consumers benefit relatively more
because the poor spend relatively more of their incomes on food.
CARDI's assessment concerns have given highest priority to
producers and especially those with small and often fragile
resource bases. Also, CARDI's programs have emphasized the use of
these resources in production systems oriented towards the two-fold
areas of: (1) export commodities, both traditional and non-
traditional and (2) food security commodities, primarily small
ruminants and roots and tubers (CARDI 1992, CARDI 1993; Rankine,
Davis, Singh and Langham).
A primary reason for convening this workshop is to explore how
best to assess current and potential technologies in terms of their
impacts. Such assessments may be used for a variety of purposes.
Given CARDI's mandate, its research and development priorities, and
its strategic plan of action (CARDI 1993) we have identified two
broadly defined, yet inclusive and interactive purposes for the
Institute's impact assessment initiatives. First, impact
assessment may be used to assist with the process of program
selection and design. Within the context of this area of concern,
the fundamental question has to do with, "What should the Institute
be doing?" Second, impact assessment may be used as a formal
process of conducting program evaluation. Within the context of
this second area of concern, the more fundamental question has to
do with, "What difference have the Institute's programs made, are
making, or are likely to make?"
Primarily for illustration purposes, we have elected to
differentiate technology impact assessment objectives into two
discrete dimensions (or processes) of program selection/design and
program evaluation. It should be recognized however, that these
two dimensions are dependent, interactive and complementary with
respect to the mission of the Institute. In short, it is our
contention that if done effectively and integratively, the two
processes (dimensions) would effectively assist CARDI in fulfilling
its mission, which is stated as:
To contribute to agricultural development through the
generation and dissemination of appropriate technology
that will benefit the Caribbean people." (CARDI, 1993,
The two dimensions of the impact assessment process function
interactively and complementarily across the four interrelated
phases of the technology diffusion process identified within the
CARDI system. Recall that these are: Phase 1--Identification,
Phase 2--Generation, Phase 3--Validation and Phase 4--Diffusion
(Rankine,Davis ,Singh, and Langham). Details of the four phases
are discussed in the report cited.
The remaining sections of this paper will consist of impact
assessment at different levels of production activities and
economic aggregation and some closing remarks.
ASSESSMENT AT THE FARM/HOUSEHOLD LEVEL
Normally, data do not exist to permit econometric estimation
of the underlying technologies at the farm/household level.
Therefore, it is necessary to rely heavily on budgets to represent
the.technologies of basic productive enterprises. Also, there are
an infinity of potential budgets which one might construct. In a
recent paper, Langham and Sama have attempted to explain what such
budgets mean. One can safely say that budgets for an enterprise,
like good friends, should be few and well chosen.
If one is assessing which farm enterprises to develop and
promote as a program, the budgets of enterprises from which the
choice is to be made should represent best practices in order to
assess them at their best potential. In short, the best practices
can be used to construct budgets that are as technically and
allocatively efficient as one can gauge for each alternative
enterprise. An enterprise is technically efficient, if given the
existing technology or state of knowledge, the output is the
maximum amount that can be technically produced from a given
combination of inputs. Resource use is allocativelv efficient if
the quantities of variable inputs used yield the highest net
economic returns to the producer and the fixed resources he or she
controls. An enterprise that satisfies both the conditions of
technical and allocative efficiency are considered to be
economically efficient. However, if one is evaluating the
effectiveness of an existing program, one should base the budgets
on performance on typical farms. This means that selection should
be based on budgets from best performing farm trials or
experimental results, and evaluation should be based on averages
from farm survey data.
Regardless of the purpose, the budgets are the basic building
blocks from which results will follow, so the construction of these
blocks is a serious undertaking. Budgets can be used to support a
rapid appraisal of a new or existing technology. Or, they can be
used to define activities for resource-allocation-type models.
Results from these latter type models can be used (as pseudo or
synthesized data) to appraise supply-side issues where estimates of
supply-side parameters are not available from econometric
measurements. We will now address some specific uses of budgets
and some helpful tools in their construction.
In a rapid appraisal approach, budgets can be used to assess
the relative profitability of alternative uses per unit of the most
limiting resource. On most small farms, this most limiting
resource is land. However, within the context of the Caribbean and
the rapid growth of the service sector, particularly tourism, the
most limiting resources in certain cases could be labor. With
minor extensions, one can use budgets to quickly and crudely
approximate the impact of a certain technology or policy on farm
profitability--especially those affecting input-output coefficients
or output and/or input prices. This use of budgets for rapid
appraisal is central to the Policy Analysis Matrix (PAM) approach
coined by Monke and Pearson and used in a training module developed
by Bivings and Gotsch for the USAID funded Agricultural Policy
Analysis Project, Phase II, managed by Abt Associates, Inc.
Our basic reservations with PAM are two. First, its name
suggests more than it delivers. It is policy analysis only in a
very narrowly defined and limited sense of the term and calling
differences between a couple of budgets a matrix is in our opinion
an overuse of jargon. Second, other jargon used in the "matrix" is
confusing and not consistent with the conventional use of terms.
The term "social" in PAM is normally used when speaking of a more
open and privately-driven free-enterprise system and "private" is
used for the current socially regulated system under which the
farmers and other entrepreneurs function. The terms should be
reversed. The use of the terms in PAM seems to be to make "social"
look good and "private" look bad. The terms seem objectionable in
the sense that it is the current social system under which
individuals must function that is the problem which generally needs
analysis. On the positive side, Bivings and Gotsch in working with
PAM have developed a fairly efficient spreadsheet approach to the
use of budgets for rapid appraisal. Their diagonalization (or in
their terms the diamondback display) of the spreadsheet and use of
common dimensions of matrices along the diagonal facilitate the
ability of an analyst to exploit the strengths of the spreadsheet
software which in their manual is the Windows-version of Lotus.
The key block-diagonal matrices in the Bivens-Gotch approach
are one representing the physical inputs and outputs defining the
budgeted enterprise, one specifying the assumptions made about the
prices of inputs and outputs, and a third for the budget
computation. The key feature is that each of these three diagonal
matrices are dimensioned precisely the same so that all formulas
used for computation can be easily copied with the "COPY" feature
of the spreadsheet software. More specifically, assume the general
block-diagonal (diamondback) configuration given in Figure 1. In
this budget, land is assumed to be the most limiting factor of
production. This is arbitrary and one can assume that the limiting
resource is the size (capital stock) of the breeding herd as we
will see in example 1.
Inputs/Unit of Land
Yield/Unit of Land
Price of Output
Cost of Production
Figure 1. Block-Representation for a Crop Budget.
The data information for each input and each output are
located in the same location in each of the three blocks. As a
consequence, one is able to multiply by formula the quantities in
the first block by the prices in the second block to obtain the
costs and returns in the third block. After specifying one
multiplication formula in the third block, one can copy this
formula into the other cells of the third block and the rows and
columns will be automatically adjusted within the spreadsheet
Three examples are presented here. The first is constructed
using Evans' survey data for sheep production in Guyana. The
individual farm budgets by Evans provide considerable insights into
ruminant production on resource-poor farms in Barbados, Guyana, and
Tobago. His basic finding was that there is not much economic
potential for small ruminants if the enterprise must return a
market wage for family labor. His work also makes it fairly clear
that ruminant production on these farms was most profitable in its
most traditional form. Perhaps the most important economic
question is not, "Why don't farmers adopt supposedly better housing
and nutritional practices?", but "Why should we as researchers
expect them to lose money with such supposedly better practices?"
Evans also. found that small ruminants raised under the more
traditional system were not confined in pastures but left to range
freely. From an environmental perspective, an interesting policy
issue worthy of further research would be the impacts of a small
ruminant production system that was profitable under a new
Example 1.--In this example, herds are classified by size to
represent three scales of operation. The three sizes are included
in each block to facilitate computation and comparisons. Twenty-
four (24) farmers were surveyed and the number in the three size
groups are 11, 7, and 6, respectively. The results are given in
Tables 1, 2, and 3 depicting the three basic blocks represented in
Figure 1. Table 3 gives the final budgets for the three sizes of
sheep enterprises. If none of the returns are allocated to
management the last line of Table 3 provides an estimate of the
value of a day of family labor in sheep production.
In conducting a rapid appraisal of a new technology for sheep
production to determine its potential impact on farms with
different herd sizes, the economist would work with the production-
oriented research scientists to modify the input-output
coefficients in Block 1 to accurately describe the technology.
This modification should be based on what the expected performance
of the technology would be when it is properly used. It should not
be based on the best research outcome obtained during
experimentation, but rather on the average of the replications for
that technology. The economist could represent the new technology
in three additional columns so that Table 1 now reflected three
columns defining the existing technology (as currently depicted in
Table 1) and three new columns defining budgets for the three size
herds under the new technology. Three additional columns would
also be added to Blocks 2 and 3. If the economist expected no
change in prices, the added columns in Block 2 would simply be a
duplication of the existing columns. Again by dimensioning the
three blocks identically, the formulas in Block 3 could simply all
be copied to carry out the computations needed to give a quick
appraisal of the new technology.
We have borrowed two budgets from the work of Taylor, Antoine,
and Smith to further demonstrate this spread-sheet approach
suggested by Bevings and Gotsch. The first of these budget is for
hot peppers in St. Lucia and the second is for papayas grown over
a 4-year cycle--again in St. Lucia. Both budgets are for pure
stands and not for a mixed-cropping environment. We will discuss
a budget for a mixed-cropping enterprise as an extension of the two
budgets used. The process by which the input-output data and
prices were specified in the budgets was not well-defined by the
authors, but the data seemingly came from a variety of sources,
including farm surveys, opinions, and assumptions. The authors did
some sensitivity analysis, mainly on the price of outputs.
Example 2.--Table 4 contains the physical input-output data for the
hot pepper budget. To demonstrate how the economist could quickly
appraise a macroeconomic policy change with this method, variable
inputs are classified into tradables (those imported) and
nontradables (from domestic sources). Here, fertilizers and
chemicals are assumed to be imported into St. Lucia. The "other
costs" category was developed mostly for convenience to recognize
these items without attempting to break them up into their
component parts. The data in the two columns are identical and the
second column is added simply to keep the dimensions of the blocks
the same--again for computational convenience via a spreadsheet
software. These headings used for the two columns of data are in
the spirit of the PAM jargon. The column with the "Private"
heading is the existing situation, and the "Social" will be used to
assess the effect of lowering the import tariff, which is assumed
to exist in St Lucia, on tradeable inputs. We further assume that
the tariff will be reduced by an amount which lowers the prices
paid by farmers by 10 percent'. With these assumptions, the
removal of the import tariff shows up in Table 5 as a 10 percent
reduction in the price of tradable inputs in the "Social" column.
We hasten to point out that this policy would change the relative
prices of tradables to non-tradable inputs and the relative prices
of tradable inputs to the price of hot peppers. These new relative
prices would encourage farmers to adjust toward the use of more
tradable relative to non-tradable inputs which would also affect
the input-output ratios in Table 4. So, our example here is a bit
naive and provides only a rough approximation in that it assumes no
input-output changes in Table 42.
'This policy scenario is simply an example. An alternative
scenario might be government action to subsidize the price of
fertilizer to the hot-pepper producer by 10 percent. The effect on
the hot-pepper producer would differ because it would change the
relative price of fertilizer and chemicals as well as fertilizer
and other (non-tradable) inputs. Also, the overall effect would
depend on how broadly (to what other enterprises) the government
offered the fertilizer subsidy.
2 The effect of changes in relative prices on the farmer's use
of inputs and outputs also serves to emphasize a weakness in the
practice of using budgets to do sensitivity analysis. For example,
a common practice is to change the price of the output over some
range to see what the effect is on net returns to the enterprise.
The results of such efforts should be viewed as rough
approximations--as should our rapid appraisal efforts.
The third block of Figure 1 is presented as Table 6 in this
example. The costs and returns given are simply the respective
multiplication of cells in the same locations in Tables 4 and 5.
Again, because all tables have the same dimension on the block-
diagonal arrangement in the spreadsheet, one can input in the
multiplication formula for one cell in Table 6 and use the copy
feature of the spreadsheet software to fill in the remaining cells.
The procedure is very fast and user friendly.
Table 7 gives a summary of the implications of the 10 percent
reduction in the import tariff on tradables in the form of a PAM.
Profits (net returns to land and management) are simply revenue
less the cost of tradable and nontradable inputs under the two
scenarios. And, the divergence is a simple subtraction of the
situation with the 10 percent reduction in import tariffs from that
with the tariff. The change would amount to a transfer from the
treasury to the farmer of EC$110.90. Summary tables of this form
are helpful in highlighting the bottom line of a technology or
The physical scientists here might now ask "Why are we
considering the effect of a policy change?" Or, "What has this got
to do with technology assessment?" We hasten to point out that
just as the physical scientist has a responsibility to design new
technologies to help farmers, the economist has a responsibility to
suggest new economic policies (institutional innovations) to help
them. The ideas of technological innovation and institutional
innovation are very close, and the methods we are discussing here
are useful for the rapid appraisal of both types of innovation. We
touched on this important linkage in the early part of the
introductory section of the paper. We economist also are a bit
sensitive here simply because our colleagues in the "hard sciences"
too often view us as having only a supportive role. CARDI needs to
be ever cognizant of this vital role of their economic and social
science technical staff in contributing to institutional
An economic analyst can also add columns to the three tables
in this example, as he or she wishes, to do sensitivity analysis on
prices or to explore "what if" type questions being considered by
researchers, planners, or policy makers. In doing this, it is
important to use the "INSERT" feature of the spreadsheet when
adding columns (or rows) to each block so as to preserve the
ability to use the "COPY" feature of the software. It is also
important to remember the shortcomings (see footnote 2) of using
budgets to do sensitivity analysis.
Sensitivity analysis can also be performed with the use of
additional blocks on the diagonal for depicting changes to be
explored. For example, we could have had only the elements in the
private columns in Tables 4, 5, and 6 and represented the "social"
prices and budget with two additional blocks (tables) on the
diagonal. Another possibility is a matrix or matrices where the
influence of selected macroeconomic variables such as the nominal
or real exchange rates, other tariff barriers, and interest rates
may be changed.
Example 3.--Tables 8, 9, and 10 are for a perennial crop example.
A 4-years papaya enterprise is used. The important point is that
all four years are included for the perennial. One could compute
the present value of the average annual stream of revenues given in
Table 10 for comparison with annual crop alternatives.
The three examples presented above were for one commodity
enterprises. If one were preparing a budget for a mixed cropping
system where the crops were interplanted, the budgets would look
much the same. The total of each input used in the mixed system
would be listed without any attempt to disaggregate them by crops.
There is just no sound conceptual way to disaggregate the input
side of jointly produced commodities. The physical output section
of block 1 and the output prices in block 2 would of course contain
additional rows--one for each crop harvested. And, the revenues
from each commodity produced in block 3 could simply be summed
(using the SUM feature of the software) to obtain the total revenue
from the mixed cropping system.
Resource-poor farmers generally include such mixed
enterprises, which could include livestock as well as crops, and
there is a need to budget such enterprises to get an idea of what
the bottom line is for the farmers using them. However, even
though as economists we can fairly easily budget a mixed
enterprise, such systems are very complex from CARDI's research and
program design perspective. From this perspective such complex
systems--produced in an array of environments which include
different soils, slopes and elevations, and access to input and
product markets--are a scientific. nightmare. The number of
variables involved are just too large to experimentally sort out
all the important effects. Some of the conceptual, operational
issues and dilemmas associated with a highly diversified
agricultural production system, such as the Caribbean, are
addressed in a paper by Davis.
On-farm trials with these complicated enterprises began in the
early-1970s and arose as a consequence of the recognition of their
importance to small farmers. Nevertheless, this recognition has
not been translated into unraveling the experimental puzzle. Work
at the University of Florida suggest that the more diversified a
system the more costly are gains in productivity, and we believe
the causal factor for this result is that more diversified systems
spread research and other technical human-capital resources over
too many problems (Habasch; Habasch, Langham, and Emerson).
On-farm trials is more nearly an exercise in learning by
doing. This method is important and T.W. Schultz's hypothesis that
traditional farms are poor but technically and allocatively
efficient, essentially rests on this important way of gaining
knowledge (Schultz, pp. 36-52)3. Nevertheless, we often forget the
wisdom and knowledge gained over generations by farmers when our
3 Several other studies have supported this hypothesis. For
some examples see the references to Tax, Hopper, and Gomes.
recommendations seemingly go unheeded. Our challenge is to make
sure the technologies we propose for our programs are of real
benefit to the farmers, and when the farmers do not accept what we
propose, we should first reevaluate our recommendations.
As indicated earlier, the use of a budget for rapid appraisal
is conceptually most sound when there is a resource which dominates
other resources in restricting production during the production
cycle. We have said nothing about risks. If time series data on
gross margins exist, one can augment the information in a budget
with an estimate of the coefficients of variation for the gross
margin. In an enterprise where crops are interplanted, the
economic analyst may be able to think of the mix of crops as a
portfolio. Indeed, using only price risk in a Cameroonian context,
Langham (pp. 87-9) found evidence that the Schultzian hypothesis
extends to the choice of crops interplanted in the sense that the
crop mixes are consistent with the concept of an efficient
portfolio for risk averse farmers. If crop insurance is available,
the premium for such insurance can permit the analyst to also
recognize the cost of other uncertainties in the budgets.
Models for a More Complete Assessment
A much more common situation on farms is where different
resources are binding at different times of the production season.
For example, during planting or harvesting of certain crops, labor
requirements can restrict the optimal size of the crop enterprise.
In such a situation, activity analysis with a mathematical-
programming-type model4 would be more appropriate for assessing the
on-farm potential of a new technology or policy--or the impact of
an existing technology. And, if data are available to estimate
variances and covariances of the gross margins of enterprises in
4 We do not include a numerical example for such a model here
because of time constraints in discussing the interesting detail of
such models. Persons with specific questions about such models are
invited to contact the authors.
the system, such models can permit more satisfactory ways to
incorporate risks into the assessment. References covering the
application of this approach are readily available in the paper by
Staal and Davis in a Cameroonian context and by Rankine, Bruce,
Pemberton and others in a Caribbean context.
Models of this type when aggregated across typical farm
situations can also provide a basis for synthesizing information on
the supply responses of farmers with respect to a new technology
(and to changes in price policies in the input and product
markets). As an example for a new technology, the model could be
run with and without the new technology for each typical-farm
situation with each run including parametric programming on the
product price. The results could be used to aggregate across
typical farms to obtain an approximation of the supply response via
the stepped supply responses from the results of parametrically
programming the models. This two stepped functions (with and
without the new technology) could be used directly in models as
discussed in the next section, or if one desired, they could each
be smoothed by ordinary least squares regression on the synthesized
data (corners of the steps) and treated as an approximation to a
continuous supply function. A third alternative would be to simply
use the stepped supply function to derive a measure of the arc-
elasticity of supply between two steps in the middle of the data.
This elasticity could then be used as demonstrated in Appendix 1.
The main point is that insights into supply responsiveness is an
essential part of the assessment processes at the market or sector
levels, as we will see in the next section.
Research resources are too scarce to permit the modeling of a
detailed grid of typical farms. This modeling requires information
on resources available for typical farm situations and enterprise
budgets describing the productive enterprises on these farms. The
physical environment of farms (as it affects input output
coefficients), economies of scale on farms (again identified by the
effect on input-output coefficients), and the proximity to markets
area (only as distance affects which commodities can be marketed)
are three of the more important determinants in identifying typical
If one partitioned each of these determinants in a particular
country into just two groups there would be 2' 8 typical farms.
Since CARDI's economic program is directed toward small-resource-
poor farms in fragile environments, such a program boundary would
permit a reduction in this number. A good rule-of-thumb for data
collection purposes is to partition farms into typical situations
with a course grid and to avoid false complexity. Every farm is
unique in some aspect and the economic analyst must abstract and
approximate the key elements operating to affect farmers' supply
response. Nevertheless, the bottom line is that enterprise budgets
are fundamental to the assessment process and are a good place to
To our knowledge, numerical results from a mathematical-
programming-type model have not been reported in a PAM framework.
However, there is no reason why one could not depict model results
before and after a technology or policy change in a PAM-type table.
Indeed, one could write accounting identities into the model to
compute such items as total revenue, cost of tradables, and cost of
nontradables to define the table.
ASSESSMENT AT OTHER LEVELS
Market and Sectoral Levels
Our discussion of assessment at the farm level in the previous
section was a partial analysis in the sense that it focused on
decisions and impacts at the farm/household level. Assessment at
the market or sectoral level, as treated here, is also partial in
the sense that it focuses only on the direct effects on producers
as an interest group, consumers as a second interest group and the
government ("treasury") as a third interest. The higher order
effects of a change in a technology on the overall economy or the
economies of trading partners are not included.
Timmer provides a good reference to a graphical approach of
the type of partial equilibrium model which we propose be used to
assess market and commodity related sub-sector and sector issues.
Gotsch also has outlined convenient spreadsheet methods of doing
calculations for such models for both market and multi-market
Market analysis requires fundamental economic information on
both sides of commodity markets. Alternative crops are in
competition for the farmers' resources and farmers adjust toward
the production of more profitable crops and away from those they
find least profitable. Farmers tend to be habit persistent and
such adjustments may take farmers a few years to work them out--
especially when perennial crops are involved. Also, different
agricultural commodities are in competition for the food budgets of
The basic pieces of economic information entering these models
1. elasticities and cross elasticities (in the multi-
market case) of supply and demand with respect to prices
of the commodities being considered,
2. prices and quantities recently experienced in these
3. any prices which are exogenous to (determined outside)
Given the openness of the Caribbean economies, trade policy is
a major instrument of agricultural pricing policy. International
prices, as modified by the exchange rate and tariff export subsidy
schedules, effectively determine domestic prices. Even prices of
products that are not directly traded internationally will tend to
respond to international influences because of substitution effects
on both supply (production) and demand (consumption) sides of the
markets. Understanding of how domestic prices are determined is
critical for technology assessment at the market or sector levels.
For modelling purposes here, we treat the Caribbean countries
as small and open to trade, and prices in world markets are treated
as exogenous to what goes on in the Caribbean markets. These
exogenously determined prices include c.i.f. (cost, insurance, and
freight to the appropriate port) prices for imported inputs and
f.o.b. (free on board at the appropriate local port of origin)
prices for commodities exported. These international border prices
of an agricultural commodity is a country's short-term opportunity
cost of a unit change in the consumption or production of an import
or export, respectively. These c.i.f. and f.o.b. border prices
need to be adjusted to reflect in-country warehousing and
distributional costs in models using them. However, for
simplification here, we will assume that these transaction costs
In small niche markets, supplies from the Caribbean can affect
world prices so the analyst should not make an assumption about
which prices are exogenous to the actions of local producers
without some knowledge of the size of the external markets.
Hopefully, most such knowledge can be borrowed from the outside,
but this is not always the case.
The strengths of the market approach are that it includes the
interest of all producers supplying the market and of all consumers
buying in it. In addition if the market is affected by government
policies one attains some information on the direct effects of
change on the treasury. The shortcomings of the approach is that
it requires estimates of supply and demand responses in the market.
And, since such information is nearly always based on historical
data, is most accurate near the means of these data, is estimated
as rates of change at the means of the data, and is often borrowed
from other studies, the analyst must show real reserve in
interpreting results for a new technology or policy--especially
when the technology or policy represents a new and yet unobserved
finite change from the existing situation.
In this paper we will consider the situation for the one-
commodity-one-market case and a graphical approach to demonstrate
the kinds of data required to support market analysis. The market
to be studied can be at many levels--local or sectoral at a
regional, national or international level.
We explore 3 cases for demonstration purposes--a national
market without trade, a national (sector) market with the country
as a net exporter, and a national (sectoral) model when the country
is a net importer. These three cases are also applicable to the
situation of market at a local or sub-national level. The key
ingredients of the puzzle are what is going on among three groups
of economic agents--(1) producers supplying the market, (2)
consumers in the market, and (3) consumers external to the market
in the case of excess supplies or producers external to the market
in the case of excess demands. The three situations are depicted
in Figure 2 (Timmer, p. 82). As long as the c.i.f. price is
o / "Supply
Figure 2. Domestic and Border Prices and Their Effects on
Imports and Exports.
greater than the price under autarky (no external trade) and this
price under autarky is greater than the f.o.b. price, the commodity
would not be traded and the local supply and demand (S and D in
Figure 2) forces would control the market. If demand shifted to D'
and supply remained stable at S, there would be an excess demand of
I and this amount would be imported in an open, free-exchange
system. If however, supply shifted to S' and demand remained
stable at D, the market would have an excess supply and an amount
X would be exported. This latter situation depicts CARDI's mission
in attempting to introduce new technologies which will lead to new
or increased excess supplies of agricultural commodities for
export. This mission was enhanced by the Agricultural Research and
Extension Project (AREP), with infrastructural and technical
support for AREP coming from the West Indies Tropical Produce
Support Project (TROPRO). Under the original terms of the AREP,
CARDI was to provide technology development and transfer (TDT)
capabilities in support of a number of designated non-traditional
agricultural exports (NTAX) and traditional agricultural exports
destined for the extra regional market (O'Donnell, Coutu, and
Malone; Moran, Bravo, and Henry).
In a small-open economy, the impacts of internal economic
behavior of producers and consumers on world prices are minimal.
This is not to say that what happens in those markets is not of
great importance to the country, but rather that internal programs
will have little impact on them.
In contrast, internal technical and institutional changes can
have great impact on domestic prices and the behavior of producers
and consumers. In developing its programs, CARDI officials must
not ignore this fact that changes in technology can affect relative
prices and economic behavior. Since everything is relative, an
enterprise cannot be made more profitable without making another
less profitable. Also, actions to make producers better off can
negatively impact consumers.
The model used to demonstrate the three cases is an abstract
simplification of the real world and is based on comparative
statics which do not explicitly include the dynamic processes of
change. The model has often been used to explore institutional
changes through price policies. However, the model is equally
applicable for demonstrating the market effects of technical change
on producers' and consumers' interests.
Case l.--In Figure 3, the domestic demand and supply of an
agricultural commodity is sketched. With competition and no trade
(situation under autarky) the market would clear at a price P0 and
quantity Q0--since the domestic quantity demanded would equal the
domestic quantity supplied. A measure of the gains to society from
the production and consumption of the commodity would be the sum of
two triangles--Consumers' surplus, P0bc, plus producers' surplus,
aP0c. These two areas represent the gains to society from the
Figure 3. Situation with no Trade (Autarky).
If a new technology shifted the supply from S to S', the new
equilibrium price and quantity would shift from PoQo to P1Q1, point
c to d. The total gain from the new technology would be the
triangular area acd. Consumers would gain the area P0cfP, from
producers and the net new area cdf. Producers would lose the area
P0cfP. to consumers and gain the net new area afd.
Studies in the United States by the Interregional Committee NC
208 (formerly IR-6) indicate that historically consumers have
captured 2/3 to 3/4 of the gains from new technology and producers
the remainder. The bottom line is that with enough time to work
out all the adjustments, consumers are the big gainers from our
agricultural research. In a PAM-type matrix, the situation
illustrated in Figure 3 could be summarized as in Table 11. Gains
are in Guyanese dollars because the computational values used as an
example were drawn from Guyana. Except for convenience, the choice
of currency was arbitrary.
Monetary values of the gains and losses can be estimated. An
example of the necessary computations is given in Appendix 1 for a
Case 2.--In this situation, and under our simplified assumption of
no transaction costs, the export (f.o.b.) price is identical to the
world market price, P,. Here it is assumed that P, is above the
domestic price under autarky so that an excess supply,.cd, prevails
in the market, and the commodity is being exported as depicted in
Figure 4. The economic rents in the system without the new
technology are the areas Pbc going to the consumers and aP.d going
to the producers. With the new technology represented by a shift
in the supply curve from S to S', the economic rents increase by
the area ade and are entirely captured by the producers.
Such rents accruing to the farmers may be difficult for them
to retain since there is a tendency for governments in a developing
country context to try to capture some of the revenues for the
treasury via a pricing policy and an agent of government acting as
a monopsonistic buyer.
that, if a new technology is developed which will shift the supply
function outward and is adopted, the producers will capture most of
the gains--at least in the short run--and that the technology will
save foreign exchange by producing a substitute for meat imports.
A supplemental enterprise is adopted because it uses surplus
resources not used by the other enterprises. In the case of sheep
this surplus resource seems to be family labor. A technology which
forces this enterprise to compete for scarce resources must provide
a more profitable alternative for the scarce resources than current
uses. It may be an appropriate strategy to plan for small
ruminants to continue as a supplemental enterprise and to look at
new technology from that perspective. For example, more productive
genetic materials which the farmers could access from a borrowed
ram may be quite helpful in this regard.
In the sections above, we stressed that the assessment models
were partial in the sense that they included only direct effects of
a technology. "What about the indirect effects as the technology
influences other economic activity in the economy?" It is a good
question and one that deserves serious thought in situations where
the new technology leads to significant change on the overall
economy. Since CARDI's mission is directed toward small resource-
poor farmers, the important effects will be those which directly
impact the families involved and normally there will not be a
serious error made in assessing only the direct impacts.
Nevertheless, we will touch briefly on the inter-sectoral impacts.
When a Leontief-type input-output model has been estimated for
an economy, one can use the matrix of interdependence coefficients
to approximate inter-sectoral effects. This method would entail
changing the vector of final demands in the input-output model and
computing the increased activity required in each sector of the
economy to satisfy the new demands.5
In recent years computable general equilibrium (CGE) models
have been used to assess the impact of structural adjustment
policies on an economy. These models are conceptually very
attractive because in an economy we are dealing with a system where
everything depends either directly or indirectly on everything
else. Nevertheless, the computable general equilibrium models are
best suited to exploring the impact of macro policy and not issues
with which CARDI is directly concerned in its technological
development and dissemination activities.
A fundamental question in an economy from the perspective of
development and competitiveness is, "How can the value of peoples'
time be increased?" And, the straight forward answer is, you
increase their productivity. Measures of total factor productivity
(TFP) provide feedback on how a system .is doing in this regard.
When the economic outputs of a system are growing faster than the
system's economic inputs, i.e., TFP is growing, there is increased
material well-being of people, as a consequence of the fact that
economic returns accrue to people. When the opposite is true there
is reason for concern about what is happening.
Another interesting question with regard to inter-sectoral
linkages is whether productivity gains in one sector spill over to
5 The computations are as follows: Let,
X = a vector of total outputs by sectors of the economy
without the technology to be estimated,
Xt = a vector of total outputs by sectors of the economy with
A = the matrix of interdependence coefficients from the
existing input-output model,
Y = the vector of final demands without the technology, and
Y, = the vector of final demands formed by adding the sectoral
requirements (both inputs and outputs) of the new
technology to Y.
(Xt X) = [(I A)'1] (Y, Y), a vector which approximates the
impact of the technology on the total output of each sector.
As an export, the commodity earns foreign exchange. Without
the new technology, the foreign exchange earnings are represented
by the rectangle cdgh. With the new technology these earnings
increase in the short-run by an amount, defg, to cefh. These
earnings are very important to every country to pay for what its
people consume from abroad. Here, consumers can benefit from the
changes in the value of their currency as a consequence of the
greater productivity of the new technology. Empirical research
indicates that an increase in the productivity of a country
relative to other countries would provide a real appreciation in
the equilibrium real exchange rate of the country (Williamson).
c d/r S*
h Co q Quantity f
Figure 4. The Effect of a New Agricultural Technology when
the Country Is an Exporter.
encourage productivity gains in other sectors. One would expect
this to be the case. A well-recognized role of the agricultural
sector in the development process is to release labor for work in
other sectors and undoubtedly productivity gains in Caribbean
agriculture makes more labor available for the rapidly growing
service sector, including the tourist industry. "Does this process
lead to productivity gains in the service sector?" The answer is
probably a yes. An equally interesting question is, "Do
productivity gains in.a sector spill over to encourage productivity
gains with international trading partners. Again the answer is
probably a yes.
Our point is not that CARDI should be doing such research but
that, even though it may be very difficult to measure indirect
effects of new technologies in the larger system, such effects are
undoubtedly important. So, the development of new knowledge which
is a form of investment in human capital is important and serious
business and CARDI has an proud and exciting supporting role to
Assessment from the Environmental Perspective
Another very critical linkage and one that is being
increasingly recognized is with the natural system. Too often we
treat agriculture as though it takes place in a closed system as we
set about doing farm level and sectoral analysis. In reality,
agriculture takes place in an open system. For an agricultural
product to be produced an out-of-equilibrium system, relative to
the surroundings, must be created and maintained. Natural
processes around the agricultural parcel tend to move the processes
on the parcel to ones that are more consistent with the ones in the
surrounds. Weeds grow, insects move in, soils wash away. Nature
abhors a differential and attempts to reduce it. To create the
"productive" out-of-equilibrium systems, farmers must continually
perturb the system by bringing inputs to the parcel and applying
work to the system (Kiker).
The natural processes are highly non-linear and interconnected
in their dynamics. Holling (p. 447) suggests "that a small set of
plant, animal and abiotic processes structure ecosystems across
scales in time and space," and that these structuring processes
"generate a discontinuous distribution of spacial structures
coupled with discontinuous frequencies". For all practical
purposes, the result is that it is not possible to predict the
consequences of particular actions applied to natural processes and
ecosystems. While after the fact, i.e., after actions are taken
and consequences appear, we can make measurements and descriptively
portray the changed situation, we do not have the necessary
knowledge to do this ex ante. Indeed, it takes time for nature to
reveal the environmental consequences of specific activities in
agriculture to us. And, it may be a long time after this
revelation before science has any practical ability to predict such
consequences for our future use.
The best we seem to be able to do today is to draw analogies.
Actions are taken and we observe some of the consequences, those
that we thought to look for, and we use the implicit correlation to
say something about similar actions to be taken in another
environmental setting. For centuries, tillers of the soil have
been aware of soil erosion associated with both crop and livestock
production techniques, and they were concerned and took actions
because they recognized that it reduced production. However, only
recently have we become concerned about the effects of soil erosion
on downstream ecosystems.
Thirty years ago Rachel Carson drew an analogy and made us
aware of the correlation between use of pesticides and the decline
of bird populations. Now we use this analogy -- i.e., pesticides
can affect ecosystems beyond the farm -- to discuss the
consequences of pesticides. We still have very little deep
knowledge of the pesticides-specific effects, but at least we are
aware that they are likely to exist. This is all to say that
assessing the impacts of specific agricultural technologies is at
best not science, but an art-form.
"How do we bring together a glimpse of what environmental
impacts might be associated with a specific agricultural technology
when used in a specific Caribbean location?" And, importantly,
"How can a useful set of information be provided given the limited
resources available to CARDI?" The following points are offered in
a quest to get at these questions:
1. The approach must be multi-disciplinary. Because of the
nature of agricultural technology/environmental interaction no
one discipline has a complete view. Rather each discipline
can bring a different, although incomplete, view. When the
different views are brought together in a structured
interactive process, the picture provided will be more
consistent than the sum of the individual perspectives. Our
suggestion is that for each technology being considered, a
group of researchers become the environmental assessment team
and follow the technology through its development and
2. Three sets of analytical boundaries should be used. For
practical reasons, we suggest that three levels of analysis be
considered, each with a different degree of specificity,
reflecting the different levels of knowledge held by scientist
in general and CARDI scientist specifically.
The first would be the farm/household level. Here the
inflows would be specified, and taking into consideration
general knowledge about the production techniques, projections
of likely unintentional outflows made. The projections may be
quantitative or qualitative depending upon information and
knowledge. Fertilizers, pesticides and soil movement are
examples of materials that would be considered. An attempt
would be made to provide an aggregate measure, either cardinal
or ordinal, of the expected outflow of materials. Given CARDI
scientists' knowledge of crops and farming systems,
information on impacts are expected to be most detailed at the
The second is the watershed level. Here the complexity
of the relationship between the potential technology and
natural process is much greater and the type of information
will be more general. The appraisal team using aggregations
of farm level information would combine this with prior
knowledge developed by researchers in other settings. The
information would typically be based on the subjective expert
opinions of the appraisal team.
The third level takes into consideration the coastal
zone. Virtually all of the Caribbean island lands are part of
a coastal zone and much of the agricultural activities in
Guyana are associated with coastal zones. This level is
included because other economic sectors (tourism and fishing,
for example) depend on coastal resources and environments, and
those considering new agricultural technologies are concerned
about possible effects on these resources and environmental
systems. Again, these systems are highly complex and the
knowledge of likely impacts from agriculture is minimal.
Appraisal will be even more tentative than for the watershed
level, but this is no reason not to organize the information
and knowledge to the degree possible given the extremely
limited CARDI resources.
3. Factors on which information will be organized will depend on
the decision context. The CARDI appraisal team would select
the categories of information to be collected and evaluated
depending on the questions that must be answered. It is
recognized that there is no universal set of information that
will provide conclusively answers to all possible questions.
The set of information to be used will be decided by the team
in response to CARDI's and other relevant organizations' need
for information in making decisions related to the research
4. The reporting format will be designed to facilitate
communications with those who must make decisions. A summary
format backed up by data and narration would allow quick
In the long-term and if the technology can be copied, all
consumers benefit as the technology is adopted around the world.
In this sense every country is on a treadmill trying to stay ahead
of the competition so as to capture early benefits from
technologies for their producers and consumers. These benefits to
consumers are in the form of increased foreign exchange earnings
and long-term benefits from cheaper food. From the producer
perspective those who are late in adapting will capture none of the
benefits. However, the late-adopters of a technology will suffer
from the lower prices associated with the increased supplies from
those producers who have adopted. This situation is why CARDI is
in such a difficult business of trying to develop technologies
targeted to farmers with small and fragile resource bases. These
farmers tend to have a strong aversion to risks and their low
financial base make them cautious adopters of new ideas. This
situation is also why as consumers of food we should all support
strong National Agricultural Research Systems (NARS) throughout the
world. Strong agricultural research systems are even more
attractive because every person gains from cheaper food and the
poor among us gain relatively more. In the case of the Caribbean,
where financial resources are scarce, territories are dispersed
geographically, and technical expertise scarce, innovative
institutional collaboration between regional research organizations
such as CARDI, NARS and International Agricultural Research Centers
(IARCs) must be fostered to address the research needs of the
Case 3.-- Finally, we will depict the situation where the c.i.f.
price, P., is below the domestic price under autarky (Figure 5).
This is the case which is perhaps closest to CARDI's small-ruminant
program. In addition, there are savings in foreign exchange--the
area ijgh in Figure 5. The other outcomes are depicted in Table
12. In this situation, the producers again capture the economic
rents. However, in this case the government cannot capture any of
the gains with a pricing policy, but some of the gains could be
captured via an income tax.
This case provides an excellent example of a situation where
a minimum of economic analysis can provide some very useful
insights into the assessment and evaluation of technology. Evans'
analysis in Guyana and other Caribbean countries showed that the
sheep enterprise was a profitable supplemental-type enterprise
a I I
0 h qg o f Quantity
Figure 5. The Effect of a New Agricultural Technology when
the Country is an Importer.
which did not compete for land and which used very little capital
within the traditional systems--which were the most profitable. In
the case of the small-ruminant program, our market model suggest
reference with the potential for the reader to check the
scientific basis of the summary information. The idea behind
the format is to provide summary information to decision
makers, while having other scientist accept the information as
a reasonable reflection, given the constraints, of the
possible consequences. (See Kiker and Lynne for an example of
This paper was written in the spirit of enhancing the dialogue
(or entering into a scholarly "conversation" if you wish) regarding
how to assess the impact of technologies on the agricultural
constituency mandated in the CARDI mission statement. The
approaches and methodologies discussed in the paper must be
appropriately seen within the context of their being operational
mechanisms or tools to assess how well CARDI's technology
development and transfer (TDT) program is contributing to the
agricultural development (and hence benefit) of the Caribbean
people. We hasten to suggest in the spirit of the "conversation",
that a major challenge to technology impact assessment is to
identify how the output of technological change contributes to the
meeting of today's needs (i.e. income growth) and how it may affect
the capacity of future generations to meet their needs (i.e.
contribute to sustainability). We also hasten to highlight the
point made earlier, that technology impact assessment is as much
art-form as science. Discussions such as we have had in this
workshop, are vital to efforts to "perfect" this art-form.
The approaches and techniques discussed are representative of
the conceptual and analytical tools that we as economists, bring to
the conversation on this important subject. We recognize that the
concepts and methodologies presented may appear formidable and
intimidating to the non-economists. Despite the multiple methods
and levels of the procedures discussed, the approaches contain
seven interactive dimensions which help to clarify the general
approach to technology impact assessment which we have suggested.
First, technology impact assessment must start with a
comprehensive description of the characteristics of the technology
or technological package that is to be evaluated. Second, the
goals of the technology development and transfer (TDT) program must
be clearly articulated. Third, indicators for and measurement of
the inputs into the TDT program must be developed and undertaken.
Fourth, indicators for and measurement of the output of the TDT
program must also be developed and undertaken. Fifth, the TDT
process must be clearly defined and articulated. Sixth, the inputs
and outputs of the TDT program must be linked in some systematic
manner. Seventh, the output of the TDT program must be
structurally linked to the larger system and the goals of the
The extent to which these seven critical dimensions can be
linked systematically within CARDI's organizational structure will
determine to a great extent, the success of the TDT program.
Although not addressed explicitly in the approaches and procedures,
we wish to highlight the importance of the goal setting and
measurement dimensions of the technology assessment exercise.
CARDI has articulated a set of goals (CARDI, 1993) and these goals
must be structurally linked to the sub-goals of its TDT program.
Also, effective data collection, data soundness, and data
processing, are components that must be accorded high priority
within the Institute.
Inputs and Output for Sheep Production in Guyana by Flock
Size: BLOCK 1.
Vet & Med
Avg. No. Ewes
Animals Purchased G$
Equip. Purchases G$
-----Averages for Flock Size--------
No. 5 10 11 $No.< 25 No. a 25
Taxes & Rents G$ 37.36 20.71 51.67
Change in stock value seems to be grossly over estimated,
especially in the largest size group. The per head increase in
values were G$ 1223, G$1126, and G$2985, respectively, for the
three size groups. G$ 123.00 = U.S.$ 1.00.
Table 2. Prices of Inputs & Outputs in Sheep Production in Guyana
by Flock Size: BLOCK 2.
Family Labor: G$/Day
-----Averages for Flock Size--------
No. 5 10 11 No.< 25 No. 2 25
150 150 150
Vet & Med.
Animal Purchase G$
Equip. Purchases G$
Taxes & Rents G$
Prices per Head:
Budget: Cost &
Size: BLOCK 3.
Returns from Sheep in Guyana by Flock
-----Averages for Flock Size--------
No. 5 10 11 SNo.< 25 No. > 25
Vet & Med
Taxes & Rents
Home Cons. G$
Change in G$
Total Revenue G$
Total Cost G$
Net Returns to Mgt G$
Net Returns to
Family Labor & Mgt G$
Max. Family Wage
S Change in stock value seems to be grossly over estimated,
especially in the largest size group. The per head increase in
values were G$ 1223, G$1126, and G$2985, respectively, for the
three size groups.
Input-Output Data for Hot Pepper Production in St. Lucia:
Labor Hours for:
Other Costs in EC$s:"
Output in lbs.
* EC$ 2.70 = U.S.$ 1.00.
Table 5. Input-Output Prices for Hot Pepper Production in St.
Lucia: BLOCK 2.
Description Private Social
------Price/Unit in EC$s--------
16-8-24, kg. 0.92 0.83
Ammon. Sulphate, kg. 0.48 0.43
Malathion, 1. 34.25 30.83
Ambush, ml. 0.37 0.33
Gramoxone, 1 15.00 13.50
Wage Rate/Hours for:
Land Clearing 3.75 3.75
Transplanting 3.75 3.75
Lining 3.75 3.75
Forking/Ridging 3.75 3.75
Nursery 3.75 3.75
Fertilizing 3.75 3.75
Weeding 3.75 3.75
Harvesting 3.75 3.75
Other 3.75 3.75
Seed/oz. 1.00 1.00
Overhead 1.00 1.00
Supervision 1.00 1.00
Interest 1.00 1.00
Transportation 1.00 1.00
Output Price/cwt. 0.71 0.71
Table 6. Budgeted Costs & Returns per Acre of Hot Peppers in St
Lucia: BLOCK 3.
Total Costs (Excluding Land)
Net Revenue to Land & Mgmt.
Table 7. A Policy Analysis Matrix for a Ten Percent Reduction in
the Import Tariff of Tradable Inputs in Hot Pepper
Production in St. Lucia.
Revenues Input Costs Profits
"Private" Prices 7100.00
"Social" Prices 7100.00
Table 8. Input-Output Data for Papaya Production in St. Lucia:
------Quantities/Acre by Year of Crop----
Labor Hours for:
Other Costs, EC$s:
Output in lbs.
Table 9. Input-Output Prices for Papaya Budget by Years: BLOCK 2.
Description ----------Prices in EC$s/Unit-------------
Year 1 Year 2 Year 3 Year 4
16-8-24, kg. 1.08 1.08 1.08 1.08
Gramoxone, 1. 15.00 15.00 15.00 15.00
Wage Rate/Hour for:
Land Clearing 3.75 3.75 3.75 3.75
Planting 3.75 3.75 3.75 3.75
Land Prep. 3.75 3.75 3.75 3.75
Lining 3.75 3.75 3.75 3.75
Soil Conserv. 3.75 3.75 3.75 3.75
Fertilizing 3.75 3.75 3.75 3.75
Weeding 3.75 3.75 3.75 3.75
Cult. Practice 3.75 3.75 3.75 3.75
Harvesting 3.75 3.75 3.75 3.75
Plants 4.00 4.00 4.00 4.00
Knapsack/tools 1.00 1.00 1.00 1.00
Overhead 1.00 1.00 1.00 1.00
Supervision 1.00 1.00 1.00 1.00
Interest 1.00 1.00 1.00 1.00
Transportation 1.00 1.00 1.00 1.00
Output Price/lb. 0.29 0.29 0.29 0.29
Table 10. Budget for 4-Year Papaya Enterprise by Years: BLOCK 3.
------------EC$s per Acre-----------------
16-8-24, kg. 108.00
Gramoxone, 1. 45.00
Labor Cost for:
Land Clearing 300.00
Land Prep 75.00
Soil Conserv. 300.00
Cult. Practice 60.00
Total Revenue 7830.00
(Excluding Land) 8439.94
Net Returns to
Land & Mgmt. -609.94
Table 11. A Summary of the Gains from the Technology Depicted in
Total Costs Producers Consumers Societal
Revenue Surplus Surplus Gains
(- PP0ce (- acg
+ QoedQ,) + QogdQ1)
(- P Pcf P1Pcd
Table 12. A Summary of the Gains from the Technology Depicted in
Total Costs Producers Consumers Societal
Revenue Surplus Surplus Gains
Computational Example for a Market Assessment
The computations demonstrated here are based on the following
assumptions regarding the demand and supply of lamb and mutton in
Guyana for 1991 (When possible, we have based these assumptions on
1. domestic production of lamb and mutton was 520 metric
tons (FAO, p.204),
2. the population of Guyana was 802,000 (World Bank),
3. per capital consumption of lamb and mutton was 2 kilograms
4. weighted average price was G$ 415.00 per kilogram (based
on the average price per head from the survey by Evans,
Tables 2 and 2B, and the average carcass weight reported
by FAO, p. 204),
5. the elasticities of domestic demand and supply were -1.0
and 0.20, respectively,
6. a new technology has been developed which will increase
the quantity supplied by 10 percent at every price,
7. the shift in supply as a consequence of the new
technology is iso-elastic at the world price, and
8. the prices of all other consumer food items and other
items produced on farms with sheep are held constant.
The first task is to estimate the demand function and, for
each of the technologies, the supply function. This is
accomplished by using the definition of the elasticities of demand
and supply, the estimates of these elasticities, the information
given on price and quantities (demanded and supplied), and the
point-slope form of the equation of a straight line. These linear
demand and supply equations provide a first-order approximation to
the unknown demand and supply equations at the price and quantities
given. The definition of the elasticity of demand and of supply,
estimates of these elasticities,and the observed prices and
quantities can be used to compute the slopes of the three functions
D: (dQ/dP)(P/Q) = -1, so dP/dQ = -P/Q = -415/1604 = -0.2587
S: (dQ/dP)(P/Q) = 0.2, so dP/dQ = P/Q/.2 = 415/520/.2 = 3.990,
and S': (dQ/dP)(P/Q) = 0.2, so dP/dQ = P/1.1Q/.2 = 415/572/.2
Combining these estimates with the respective prices and
quantities, one can estimate the intercepts of the functions. We
will demonstrate with the demand function:
D: P = a 0.2587Q, and since P = 415 and Q = 1604 we have
415 = a (0.2587)(1604), or a = 415 + (0.2587)(1604) = 830.
The values for the demand and supply parameters were all computed
by a spreadsheet without rounding and are given in Table 13 where
the results were rounded to 4 places.
Table 13. Demand and Supply Parameters for the Hypothetical Market
for Lamb and Mutton in Guyana.
Demand, D Supply, S Supply, S'
Intercept 830.0000 -1660.0000 -1660.0000
Slope -0.2587 3.9904 3.6276
These demand and supply functions have been plotted in Figure
6. The areas in Figure 6 representing the various interests are
presented in Table 14. The numerical values in the various cells
are easy to compute in this example because they are all rectangles
or triangles as defined by the coordinates of the corners presented
in Table 14. These coordinates are as given in the following text
Point in Fig. 6
When the demand and supply functions are non-linear the areas
would have to be computed by integration. We could have chosen to
estimate log-linear demand and supply equations which are non-
linear in price and quantity. When this is done the integral under
the demand function is unbounded and one cannot estimate consumer
surplus without fixing a finite maximum price below which one can
approximate consumer surplus--or a small but positive quantity
above which the surplus can be estimated.
Table 14. A Summary of the Computations of Gains from a
Hypothetical Technology in Sheep Production in Guyana.
Costs Producers Consumers
(PS + CS)
OP ti =
OP kj =
Difference 21,580 2,158 19,422 0
Savings in foreign exchange = hktg = (50)(415) 20,750
Savings in foreign exchange = hktg = (50)(415) 20,750
(in 1000 kgs.)
P = 830 0.2587 Q
P = -1660 + 3.9904 Q
P = -1660 + 3.6276 Q
Figure 6. A Numeric Example of the Computations of the Market
Benefits of a Technology when the Country is an
Exporter: Sheep in Guyana.
Bivings, Leigh, and Carl H. Gotsch. Agricultural and Natural
Resources Policy Analysis Course: The Policy Analysis Matrix
(Agricultural Price Policy). Volume 1. Cambridge: Abt
Associates, Inc., July, 1991.
CARDI. Annual Report. 1991-1992. Trinidad, WI, 1992.
CARDI. Strategic Plan. 1994-2004 (Draft). Trinidad, WI, May 1993.
Carson, Rachael. Silent Spring. Boston: Houghton Miffin, 1962.
Davis, Carlton G. "Product-Product Dimension of Agricultural
Diversification Strategies in the Caribbean Community:
Prospects and Dilemmas." Agricultural Diversification:
Prospects and Strategies. ed. Carlisle A. Pemberton.
Proceedings of the Nineteenth West Indies Agricultural
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