APPROACHES TO TECHNOLOGY IMPACT
ASSESSMENT IN THE CARIBBEAN AGRICULTURAL
RESEARCH AND DEVELOPMENT INSTITUTE
Max R. Langham, Carlton G. Davis
and Clyde F. Kiker
IW93-23 October 1993
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 INSTiITE*
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 def ined 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 lif e 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 (PinstrupAndersen 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 of ten, these
people left behind are the poorest of the poor, and historically,
*Paper prepared f or 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 nontraditional 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 complementarv 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 allocatively-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
Jon 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 -allocat ion- 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
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 Tradables:
Nontradables: Other Costs:
Yield/Unit of Land
BLOCK 2 Prices/Unit Tradables: Nontradables: Other Costs: Price of Output
BLOCK 3 Cost of Production Tradables: Nontradables: Other:
Total Revenue: Added Rows
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 software.
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 lef t 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 technological regime.
Exa=le 1.--In this example, herds are cla ossified by size to represent three scales of operation. The three sizes are included in each block to facilitate computation and comparisons. Twentyfour (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 production7
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 tarif f will be reduced by an amount which lowers the prices paid by f armers 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 f armers 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 4'.
'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.
I The ef fect of changes in relative prices on the f armer' 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 blockdiagonal 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$11Q.90. Summary tables of this form
are helpful in highlighting the bottom line of a technology or policy change.
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 innovation.
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 f or 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 mathematicalprogramming- type model' 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
I 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 arcelasticity 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 farm enterprises.
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-resourcepoor 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 start.
To our knowledge, numerical results from a mathematicalprogramming-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 situations.
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, dif f erent agricultural commodities are in competition for the food budgets of consumers.
The basic pieces of economic information entering there models are:
1. elasticities and cross elasticities (in the multimarket 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 are zero.
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 onecommodity-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 = 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
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 19
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 I.--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 P. 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'I surplus, P~bc, plus producers' surplus, aP~c. These two areas represent the gains to society from the market.
0 Go 01 Quantity
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 POQ0 to P1Q, 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 P0cf P. 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 simplified example.
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.
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).
Po OWam--- .
h Co q Quantity f
Figure 4. The Effect of a New Agricultural Technology when
the Country Is an Exporter.
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 f armers 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 region.
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
mo --W-.- -' ,,
0 h q Co 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
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 resourcepoor 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.'
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?"1 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
SThe computations are as follows: Let,
X =a vector of total outputs by sectors of the economy
without the technology to be estimated,
=t 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
=t the vector of final demands formed by adding the sectoral
requirements (both inputs and outputs) of the new
technology to Y.
(Xt X) = E (I A)-1] (Y Y) a vector which approximates the impact of the technology on the total output of each sector.
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 play.
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. Holding (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 ef f ects 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 30
reference with the potential for the reader to check the scientific basis of the suimmary 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 Qoals 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 Qoals of the program.
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.
Table 1. Inputs and Output for Sheep Production in Guyana by Flock
Size: BLOCK 1.
-----Averages for Flock Size-------Description: Unit No. 5 10 11 SNo.< 25 No. a 25
Family Labor: Man-days 159.5 237.4 380.2
Hired Labor: Man-days 0.0 0.0 0.0
Concentrate lbs. 429.1 617.6 709.3
Molasses gal. 2.7 0.0 17.7
Vet. Visits No./Yr. 2.5 1.7 0.3
Deworming No./Yr. 1.4 1.7 1.2
Vet & Med G$ 817.27 1821.43 3416.67
Animal Theft No./Yr. 0.4 1.6 3.5
Avg. No. Ewes Head 7.0 17.6 43.7
Depreciation G$ 1601.64 2560.00 2366.67
Animals Purchased G$ 709.09 428.57 3166.67
Equip. Purchases G$ 22.73 64.29 366.67
Taxes & Rents G$ 37.36 20.71 51.67
Sales No./Yr. 1.6 6.0 17.5
Home Cons. No./Yr. 0.5 0.4 2.8
Donation No./Yr. 0.2 0.1 1.3
Change in G$ 8563.18 19,780.1 130,343.80
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.
.-----Averages for Flock Size-------Description: Unit No. 5 10 11 5No.< 25 No. 2 25
Family Labor: G$/Day 150 150 150
Hired Labor: G$/Day 150 150 150
Concentrate G$/lb. 3.24 2.10 2.76
Molasses G$/gal. 6.00 6.00 6.00
Vet & Med. G$ 1.00 1.00 1.00
Animal Theft G$/Hd. 4641.00 4090.00 4155.00
Depreciation G$ 1.00 1.00 1.00
Animal Purchase G$ 1.00 1.00 1.00
Equip. Purchases G$ 1.00 1.00 1.00
Taxes & Rents G$ 1.00 1.00 1.00
Prices per Head:
Sales G$/Hd. 4641.00 4090.00 4155.00
Home Cons. G$/Hd. 4641.00 4090.00 4155.00
Donation G$/Hd. 4641.00 4090.00 4155.00
Change in G$ 1.00 1.00 1.00
Table 3. Budget: Cost & Returns from Sheep in Guyana by Flock
Size: BLOCK 3.
-----Averages for Flock Size-------Description: Unit No. 5 10 11 5No.< 25 No. > 25
Family Labor: G$ 23,931.82 35,614.29 57,025.00
Hired Labor: G$ 0.00 0.00 0.00
Concentrate G$ 1389.00 1297.57 1955.17
Molasses G$ 16.36 0.00 106.00
Vet & Med G$ 817.27 1821.43 3416.67
Animal Theft G$ 1687.64 6427.14 14,542.50
Depreciation G$ 1601.64 2560.00 2366.67
Animal Purchase G$ 709.09 428.57 3166.67
Equip. Purchases G$ 22.73 64.29 366.67
Taxes & Rents G$ 37.36 20.71 51.67
Sales G$ 7594.36 24,540.00 72,712.50
Home Cons. G$ 2531.45 1752.86 11,772.50
Donation G$ 843.82 584.29 5540.00
Change in G$ 8563.18 19,780.14 130,343.83
Total Revenue G$ 19,532.82 46,657.28 220,368.83
Total Cost G$ 30,212.91 48,234.00 82,997.00
Net Returns to Mgt G$ -10,680.00 -1576.72 137,371.83
Net Returns to Family Labor & Mgt G$ 13,251.72 34,037.57 194,396.83
Max. Family Wage
Rate/Day G$ 83.08 143.38 511.30
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$2985, respectively, for the
three size groups.
Table 4. Input-Output Data for Hot Pepper Production in St. Lucia:
Description Private Social
16-8-24, kgs. 500.00 500.00
Ammon. Sulphate, kgs. 385.00 385.00
Malathion, 1. 1.00 1.00
Ambush, ml. 1000.00 1000.00
Gramoxone, .1. 4.00 4.00
Labor Hours for:
Land Clearing 80.00 80.00
Transplanting 120.00 120.00
Lining 24.00 24.00
Forking/Ridging 160.00 160.00
Nursery 176.00 176.00
Fertilizing 40.00 40.00
Weeding 160.00 160.00
Harvesting 360.00 360.00
Other 24.00 24.00
Seed, ozs. 10.00 10.00
Other Costs in EC$s:a
Overhead 536.13 536.13
Supervision 206.12 206.12
Interest 337.83 337.83
Transportation 565.00 565.00
Output in lbs. 10000.00 10000.00
a 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$s-------Tradables:
16-8-24, kg. 0.92 0.83
Ammon. Sulphate, kg. 0.48 0.43
Malathion, 1. 34.25 30.83
Ambush, ml. 0.37 0.33
Gramoxone, 1 15.00 13.50
Wage Rate/Hours for:
Land Clearing 3.75 3.75
Transplanting 3.75 3.75
Lining 3.75 3.75
Forking/Ridging 3.75 3.75
Nursery 3.75 3.75
Fertilizing 3.75 3.75
Weeding 3.75 3.75
Harvesting 3.75 3.75
Other 3.75 3.75
Seed/oz. 1.00 1.00
Overhead 1.00 1.00
Supervision 1.00 1.00
Interest 1.00 1.00
Transportation 1.00 1.00
Output Price/cwt. 0.71 0.71
Table 6. Budgeted Costs & Returns per Acre of Hot Peppers in St
Lucia: BLOCK 3.
Description Private Social
16-8-24 460.00 414.00
Ammon. Sulphate 184.80 166.32
Malathion 364.25 30.83
Ambush 370.00 333.00
Gramoxone 60.00 54.00
Land Clearing 300.00 300.00
Transplanting 450.00 450.00
Lining 90.00 90.00
Forking/Ridging 8.00 8.00
Nursery 660.00 660.00
Fertilizing 150.00 150.00
Weeding 600.00 600.00
Harvesting 1350.00 1350.00
Other 90.00 90.00
Seed 10.00 10.00
Overhead 536.13 536.13
Supervision 206.12 206.12
Interest 337.83 337.83
Transportation 565.00 565.00
Total Revenue 7100.00 7100.00
Total Costs (Excluding Land) 6462.13 6351.23
Net Revenue to Land & Mgmt. 637.87 748.77
Table 7. A Policy Analysis Matrix for a Ten Percent Reduction in
the Import Tariff of Tradable Inputs in Hot Pepper
Production in St. Lucia.
Revenues Input Costs Profits
----------------------- EC$s---------------------"Private" Prices 7100.00 1109.05 5353.08 637.87
"Social" Prices 7100.00 998.15 5353.08 748.77
Divergence 0.00 110.90 0.00 -110.90
Table 8. Input-Output Data for Papaya Production in St. Lucia:
Description ------Quantities/Acre by Year of Crop---Year 1 Year 2 Year 3 Year 4
16-8-24, kgs. 100.00 100.00 100.00 100.00
Gramoxone, 1. 3.00 3.00 3.00 3.00
Labor Hours for:
Land Clearing 80.00 0.00 0.00 0.00
Planting 120.00 0.00 0.00 0.00
Land Prep 20.00 0.00 0.00 0.00
Lining 16.00 0.00 0.00 0.00
Soil Conserv. 80.00 0.00 0.00 0.00
Fertilizing 32.00 32.00 32.00 32.00
Weeding 40.00 40.00 40.00 40.00
Cult.Practice 16.00 16.00 16.00 16.00
Harvesting 416.00 320.00 160.00 88.00
Plants 436.00 0.00 0.00 0.00
Knapsack/tools 525.00 0.00 0.00 0.00
Other Costs, EC$s:
Overhead 404.70 59.30 59.30 59.30
Supervision 202.35 29.65 29.65 29.65
Interest 365.89 53.61 53.61 53.61
Transport 1460.00 960.00 560.00 360.00
Output in lbs. 27000.00 17000.00 9000.00 1000.00
Table 9. Input-Output Prices for Papaya Budget by Years: BLOCK 2.
Description ----------Prices in EC$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.
Description ------------EC$s per Acre ----------------Year 1 Year 2 Year 3 Year 4
16-8-24, kg. 108.00 108.00 108.00 108.00
Gramoxone, 1. 45.00 45.00 45.00 45.00
Labor Cost for:
Land Clearing 300.00 0.00 0.00 0.00
Planting 960.00 0.00 0.00 0.00
Land Prep 75.00 0.00 0.00 0.00
Lining 60.00 0.00 0.00 0.00
Soil Conserv. 300.00 0.00 0.00 0.00
Fertilizing 120.00 120.00 120.00 120.00
Weeding 150.00 150.00 150.00 150.00
Cult. Practice 60.00 60.00 60.00 60.00
Harvesting 1560.00 1200.00 600.00 330.00
Plants 1744.00 0.00 0.00 0.00
Knapsack/tools 525.00 0.00 0.00 0.00
Overhead 404.70 59.30 59.30 59.30
Supervision 202.35 29.65 29.65 29.65
Interest 365.89 53.61 53.61 53.61
Transport 1460.00 960.00 560.00 360.00
Total Revenue 7830.00 4930.00 2610.00 290.00
(Excluding Land) 8439.94 2729.56 1785.56 1315.56
Net Returns to
Land & Mgmt. -609.94 2200.44 824.44 -1025.56
Table 11. A Summary of the Gains from the Technology Depicted in
Total Costs Producers Consumers Societal
Revenue Surplus Surplus Gains
-----------------------G$------------------------Current Tech. OP0cQ0 0acQ0 aP0c P0bc abc
New Technology OPIdQj OadQ1 aP1d P1bd abd
Difference (- PP0ce (- acg (- P1Pocf P1P0cd acd
+ Q0edQ1) + Q0gdQ1) + afd)
Table 12. A Summary of the Gains from the Technology Depicted in
Total Costs Producers Consumers Societal
Revenue Surplus Surplus Gains
----------------------- G$ ------------------------Current Tech. OP ih Oaih aP i Pwbe abei
New Technology OPjg Oajg aPWj P)be abej
Difference ijgh (- Oaih aij 0.0 aij
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 published information):
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 capita 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 SIopes of the three functions as follows:
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 table:
Point in Fiq. 6 Ouantity Price
a 0 -1660
Pw 0 415
b 0 830
C 571.8841 682.0375
d 625.2653 668.2263
e 1604 415
f 1604 0
g 572 0
Qo 571.8841 0
h 520 0
i 457.6 0
j 416 0
k 520 415
1 572 415
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 nonlinear 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.
PS CS (PS + CS)
Situation Total Costs Producers Consumers Societal
Revenue Surplus Surlus Gains
-------------------- 1000 G$-----------------------New Technology 0Pw g = itg = OPwti = Pbe = (PS+CS)=
237,380 23,738 213,642 332,830 546,472
Current Tech. 0Pwkh = jkh = 0Pwkj = Pwbe = (PS+CS)=
215,800 21,500 194,220 332,830 527,050
Difference 21,580 2,158 19,422 0 19,422
Savings in foreign exchange = hktg = (50)(415) = 20,750
hg f Quantity
(in 1000 kgs.)
D: P = 830 0.2587 Q S: P -1660 + 3.9904 Q S': 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.
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